Information processing apparatus, information processing method, and non-transitory computer readable storage medium

ABSTRACT

An information processing apparatus includes: a model generation unit configured to generate a prediction model for calculating a prediction value related to a probability that a user performs an action on the Internet by operating a user terminal, for each group created by grouping users based on user information; a model selection unit configured to select a prediction model suited to the user from the prediction models; and a prediction value calculation unit configured to calculate the prediction value by using the prediction model selected by the model selection unit.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to and incorporates by referencethe entire contents of Japanese Patent Application No. 2014-191931 filedin Japan on Sep. 19, 2014.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an information processing apparatus,information processing method, and non-transitory computer-readablestorage medium that predicts an action that a user performs on theInternet by operating a user terminal.

2. Description of the Related Art

Information providing apparatuses that provide advertisements to usersvia the Internet have been conventionally known (see Japanese Laid-openPatent Publication No. 2009-193465).

Such information providing apparatuses include one that calculates aprediction value of a probability that a user clicks an advertisement ona web page and provides the user with an advertisement in accordancewith the calculated prediction value for the purpose of improvingadvertising effects. In order to calculate the prediction value, aprediction model is used which calculates a prediction value based onfeatures indicating a user attribute, relativity between the userattribute and the advertisement, and the like.

However, a prediction model common to all users has been conventionallyused to calculate a prediction value. Hence, the prediction model isrequired to include an enormous number of features to handle usershaving various attributes. Consequently, there is a problem that thetime to calculate the prediction value is long.

SUMMARY OF THE INVENTION

It is an object of the present invention to at least partially solve theproblems in the conventional technology.

According to one aspect of an embodiment, an information processingapparatus includes a model generation unit configured to generate aprediction model for calculating a prediction value related to aprobability that a user performs an action on the Internet by operatinga user terminal, for each group created by grouping users based on userinformation; a model selection unit configured to select a predictionmodel suited to the user from the prediction models; and a predictionvalue calculation unit configured to calculate the prediction value byusing the prediction model selected by the model selection unit.

The above and other objects, features, advantages and technical andindustrial significance of this invention will be better understood byreading the following detailed description of presently preferredembodiments of the invention, when considered in connection with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating the configuration of an informationprocessing apparatus according to a first embodiment;

FIG. 2 is a flowchart illustrating a model assignment process in thefirst embodiment;

FIG. 3 is a flowchart illustrating a prediction process in the firstembodiment; and

FIG. 4 is a diagram illustrating the configuration of an informationprocessing apparatus according to a second embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS First Embodiment

A first embodiment is described hereinafter based on the drawings.

Information Processing Apparatus

FIG. 1 is a diagram illustrating the configuration of an informationprocessing apparatus according to the first embodiment. An informationprocessing apparatus 1 of the first embodiment is configured of acomputer, and includes a storage device 10 and a control device 20. Interms of the information processing apparatus 1, a plurality of userterminals 2 is connected to a network 3 that can be connected forcommunication.

Storage Device

The storage device 10 is configured of an HDD (Hard Disk Drive), a flashmemory, or the like. Various programs and pieces of information that arenecessary to operate the control device 20 are stored in the storagedevice 10.

The storage device 10 includes a model storage unit 11, an actionhistory storage unit 12, a model assignment information storage unit 13,and a related information storage unit 14.

A prediction model for calculating a predicted CTR, which is aprediction value of a probability (CTR: Click-Through rate) that aspecific user clicks a specific advertisement on a web page, is storedin the model storage unit 11.

The prediction model is shown in, for example, the following equation(1). In equation (1), A and B represent a constant. y is shown in thefollowing equation (2). n represents a variable. Xn represents thenumeric features value. Wn represents a weighted coefficient thatdetermines the degree of contribution in which the Xn contribute to thepredicted CTR.

$\begin{matrix}{{{Predicted}\mspace{14mu} {CTR}} = \frac{1}{1 + {\exp \left( {{- 1}*\left( {{Ay} + B} \right)} \right)}}} & (1) \\{y = {\sum{{Wn}*{Bn}}}} & (2)\end{matrix}$

The features are various pieces of information related to a user and anadvertisement, and is information indicating, for example, a userattribute, a similarity between a delivery target page and anadvertisement, relativity between the user attribute and theadvertisement, information on the advertisement itself, and a pastdelivery record.

The prediction model is not necessarily one that obtains the CTR itselfas long as it is a prediction model that obtains a prediction valuerelated to the CTR. For example, the prediction model may obtain y shownin equation (2).

A prediction model (also referred to as local model) generated for eachgroup created by grouping users based on user attributes, and aprediction model (also referred to as common model) common to all thegroups are stored in the model storage unit 11.

For example, a housewife model generated targeting a group ofhousewives, and a rich model generated targeting a group of wealthypeople can be illustrated as the local model.

A click history (configuring an action history), which indicates aresult of clicks on a delivered advertisement when a user accessed apredetermined webpage in the past, is accumulated in the action historystorage unit 12 (configuring an action history storage unit). The clickhistory includes pieces of information such as user IDs (Identifiers:identification information), web page IDs, advertisement IDs, thepresence or absence of a click, and the date and time of a click.

The click history is accumulated in the action history storage unit 12,associated with a prediction model of either the local model or thecommon model.

Model assignment information where a user ID is associated with aprediction model assigned to the user by a model assignment processdescribed below is stored in the model assignment information storageunit 13.

Related information where features value acquired by the modelassignment process described below is associated with the predictionmodel assigned by the model assignment process is accumulated in therelated information storage unit 14.

Control Device

The control device 20 controls the operation of the informationprocessing apparatus 1, and is configured of a computing device such asa CPU (Central Processing Unit).

The computing device executes an information processing program storedin the storage device 10. Accordingly, the control device 20 functionsas a model assignment unit 21, an action history storage control unit22, a model generation unit 23, a model selection unit 24, a predictionvalue calculation unit 25, and a delivery unit 26.

The model assignment unit 21 uses a history of actions that the userperformed on the Internet by operating the user terminal to test theprediction models and assign a prediction model suited to the user.

More specifically, the model assignment unit 21 assigns, to each user, aprediction model suited to the user from the common model and the localmodels created on a group by group basis. Moreover, the model assignmentunit 21 stores, in the model assignment information storage unit 13,model assignment information that associates the user with the assignedprediction model.

Moreover, the model assignment unit 21 reads an accumulated clickhistory for each user from the action history storage unit 12, andacquires features value based on the read click history. The modelassignment unit 21 accumulates, in the related information storage unit14, related information where the acquired features value is associatedwith the assigned prediction model.

For example, the model assignment unit 21 uses features value acquiredfrom the history of the actions that the user performed on the Internetby operating the user terminal to test the prediction models, assigns aprediction model suited to the user, generates related information wherethe features value is associated with the assigned prediction model, andaccumulates the related information in a storage device, and after therelated information is accumulated in the storage device for apredetermined period, assigns a prediction model suited to the userbased on the accumulated related information.

For other example, the model assignment unit 21 assigns a predictionmodel suited to the user based on the related information indicating arelationship between the features value related to the actions that theuser performed on the Internet by operating the user terminal, and theprediction model.

The action history storage control unit 22 accumulates, in a storagedevice, an action history being a history of actions that the userperformed on the Internet by operating the user terminal.

Specifically, the action history storage control unit 22 associates theaction history with the prediction model selected by the model selectionunit 24 and accumulates the action history in the storage device.

When having acquired a click history of a delivered advertisementobtained when the user accessed a predetermined web page, the actionhistory storage control unit 22 (configuring an action history storagecontrol unit) associates the acquired click history with a predictionmodel selected by the model selection unit 24 described below andaccumulates the click history in the action history storage unit 12.

The model generation unit 23 generates a prediction model forcalculating a prediction value related to a probability that a userperforms an action on the Internet by operating a user terminal, foreach group created by grouping users based on user information.

Specifically, the model generation unit 23 generates each predictionmode based on its corresponding click history accumulated in the actionhistory storage unit 12, and stores the prediction model in the modelstorage unit 11.

More specifically, the model generation unit 23 acquires multiple pairsof features value and a click track record based on the accumulatedclick history, learns the degree of contribution (Wn) of the features toa predicted CTR, and generates a prediction model.

For example, the model generation unit 23 generates a common modelcommon among the groups, as the prediction model. For other example, themodel generation unit 23 generates the prediction model based on acorresponding action history accumulated in the storage device.

For other example, the model generation unit 23 generates the predictionmodel for each group created by grouping the users based on userattributes.

Here, the model generation unit 23 generates a prediction modelregularly to overwrite and store the newly generated prediction model inthe model storage unit 11.

The model selection unit 24 selects a prediction model suited to theuser from the prediction models.

Specifically, the model selection unit 24 selects a prediction modelsuited to the user based on the model assignment information stored inthe model assignment information storage unit 13, or the relatedinformation accumulated in the related information storage unit 14.

More specifically, the model selection unit 24 selects a predictionmodel suited to the user based on the related information indicating therelationship between the features value related to the actions that theuser performed on the Internet by operating the user terminal, and theprediction model.

For example, the model selection unit 24 selects the prediction modelassigned to the user by the model assignment unit 21.

The prediction value calculation unit 25 calculates the prediction valueby using the prediction model selected by the model selection unit 24.

Specifically, when having acquired page access information indicatingthat the user accessed the predetermined web page, the prediction valuecalculation unit 25 uses the prediction model selected by the modelselection unit 24 to calculate a predicted CTR for each advertisementbeing a candidate for delivery.

The delivery unit 26 determines and delivers an advertisement to bedelivered to the user, based on the predicted CTR calculated by theprediction value calculation unit 25.

For example, the delivery unit 26 delivers, to the user, anadvertisement with the highest predicted CTR among the deliverycandidate advertisements.

Model Assignment Process

Next, the model assignment process is described.

FIG. 2 is a flowchart illustrating the model assignment process. Themodel assignment process is executed at intervals of a predeterminedperiod, for example, one week.

As illustrated in FIG. 2, when the model assignment process is executed,the model assignment unit 21 selects one user from users with clickhistories accumulated in the action history storage unit 12 (Step S11).

Next, the model assignment unit 21 reads and acquires the accumulatedclick history of the selected user from the action history storage unit12 (Step S12), and further acquires the features value based on theacquired click history (Step S13).

Next, the model assignment unit 21 uses the acquired features value totest a plurality of prediction models stored in the model storage unit11, and assigns a prediction model suited to the user (Step S14).

Specifically, the model assignment unit 21 inputs the acquired featuresvalue for each prediction model, and calculates a predicted CTR. Themodel assignment unit 21 then compares the calculated predicted CTR andan actual value obtained from the acquired click history to assign aprediction model suited to the user from the plurality of predictionmodels.

For example, the model assignment unit 21 assigns, to the user, aprediction model having the calculated predicted CTR closest to theactual value.

In other words, the model assignment unit 21 assigns a suited localmodel to the user if any of the local models is suited, and assigns thecommon model to the user if no local model is suited and the commonmodel is suited.

Next, the model assignment unit 21 stores, in the model assignmentinformation storage unit 13, the model assignment information where theuser is associated with the assigned prediction model (Step S15). If themodel assignment information corresponding to the user is already storedin the model assignment information storage unit 13, the old modelassignment information is overwritten with the new model assignmentinformation.

Furthermore, the model assignment unit 21 accumulates, in the relatedinformation storage unit 14, related information where the acquiredfeatures value is associated with the assigned prediction model (StepS16).

Next, the model assignment unit 21 determines whether or not all theusers having a click history accumulated in the action history storageunit 12 have been selected (Step S17).

If having determined to be NO in Step S17, the model assignment unit 21returns the processing to step S11, and re-executes the processing fromSteps S11 to S17. When all the users were selected and it has beendetermined to be YES in Step S17, the model assignment process ends.

Prediction Process

Next, the prediction process is described.

FIG. 3 is a flowchart illustrating the prediction process. Theprediction process is executed when, for example, a user accessed apredetermined web page.

As illustrated in FIG. 3, when the prediction process is executed, theprediction value calculation unit 25 acquires page access informationindicating the user accessed the predetermined web page, and acquiresfeatures value for each delivery candidate advertisement, based on theacquired page access information (Step S21).

Next, the prediction value calculation unit 25 determines whether or notmodel assignment information of the target user is stored in the modelassignment information storage unit 13 (Step S22).

If having determined to be YES in Step S22, the prediction valuecalculation unit 25 selects a prediction mode associated with the modelassignment information (a prediction model of either the local or commonmodel) (Step S23).

On the other hand, if having determined to be NO in Step S22, theprediction value calculation unit 25 selects a prediction model withhigh relativity with the acquired features value based on the relatedinformation accumulated in the related information storage unit 14.

In other words, if there is a local model with high relativity with theacquired features value, the prediction value calculation unit 25selects the local model, and if not, the prediction value calculationunit 25 selects the common model.

After the processing of Step S23 or Step S24, the prediction valuecalculation unit 25 uses the prediction model selected in Step S23 orS24 to calculate a predicted value based on the acquired features valuefor each delivery candidate advertisement (Step S25).

Next, the delivery unit 26 determines an advertisement to be deliveredto the user based on the predicted CTR calculated for each deliverycandidate advertisement. For example, the delivery unit 26 determines anadvertisement with the highest predicted CTR as a deliveryadvertisement. The delivery unit 26 then delivers the advertisement tothe user (Step S26). A plurality of advertisements may be selected to bedelivered.

Next, the action history storage control unit 22 acquires a clickhistory for the delivered advertisement, and accumulates the acquiredclick history in the action history storage unit 12 (Step S27).

If the prediction model selected in Step S23 or S24 is a local model,the action history storage control unit 22 associates the acquired clickhistory with the common model and the selected local model to accumulatethe acquired click history in the action history storage unit 12.

Moreover, if the selected prediction model is the common model, theaction history storage control unit 22 associates the acquired clickhistory with the common model to accumulate the acquired click historyin the action history storage unit 12.

The control device 20 then ends the prediction process.

Operation and Effect of the First Embodiment

A predicted CTR is calculated based on a prediction model generated foreach group (a local model). Accordingly, the number of features to beused for calculation can be reduced, and the time to calculate apredicted CTR can be reduced as compared to a case where a predicted CTRis calculated using the common.

Moreover, a predicted CTR is calculated using a local model suited to auser. Accordingly, the accuracy of a predicted CTR can be improved ascompared to the case where a predicted CTR is calculated using thecommon model targeting users with various attributes.

The model generation unit 23 generates the common model common amonggroups, as the prediction model. According to this, a predicted CTR canbe calculated with the common model also for users who do not belong toany groups.

The model assignment unit 21 uses a click history of a user to test aprediction model generated for each group, and accordingly assigns aprediction model suited to the user. The model selection unit 24 thenselects the prediction model assigned to the user by the modelassignment unit 21.

According to this, a prediction model can be selected based on a user'sclick track record. Accordingly, a prediction model suited to the usercan be accurately selected.

The model selection unit 24 selects a prediction model suited to a userbased on related information where features value is associated with aprediction model, if there is no model assignment information of theuser in the model assignment information storage unit 13.

According to this, a suited prediction model can be selected also forusers having no click history accumulated and no model assignmentinformation.

Moreover, there is no need to test prediction models. Accordingly, aprediction model suited to a user can be selected immediately, and theprocessing load of the test on the control device 20 can be reduced.

The action history storage control unit 22 associates the user's clickhistory with the prediction model selected by the model selection unit24 and accumulates the click history in the action history storage unit12. The model generation unit 23 generates each prediction model basedon its corresponding click history accumulated in the action historystorage unit 12.

According to this, as the click histories are accumulated, learninginformation for generating each prediction model is increased.Accordingly, the prediction accuracy of each prediction model can beimproved.

The groups are created by grouping based on user attributes. Accordingto this, for example, a manager of the information processing apparatus1 can perform grouping effectively in accordance with experience if themanager knows the relationship between a user attribute and a CTR fromexperience.

Second Embodiment

Next, a second embodiment is described based on the drawings.

In the first embodiment, groups of users are predetermined by, forexample, a manager of the information processing apparatus 1. However,in the second embodiment, users are grouped automatically based on userinformation, using a Dirichlet process being a statistical technique.The other configurations are similar to those of the first embodiment.

FIG. 4 is a diagram illustrating the configuration of an informationprocessing apparatus of the second embodiment.

As illustrated in FIG. 4, a control device 20A of an informationprocessing apparatus 1A of the second embodiment includes a groupingunit 27 in addition to a model assignment unit 21, an action historystorage control unit 22, a model generation unit 23, a model selectionunit 24, a prediction value calculation unit 25, and a delivery unit 26,which are the same as those of the first embodiment.

The grouping unit 27 groups the users based on the user information,using a Dirichlet process being a statistical technique.

Specifically, the grouping unit 27 sets a value of a parameter todetermine the degree of gathering in the Dirichlet process in severallevels, and performs several types of groupings.

More specifically, the grouping unit 27 groups users using a Dirichletprocess, based on user information (such as user attributes and thenumber of click histories (the number of logs) accumulated in the actionhistory storage unit 12) being information related to the users.

For example, the grouping unit 27 performs several types of groupingswhere the value of a parameter (also referred to as hyperparameter) thatdetermines the degree of gathering in the Dirichlet process is set inseveral levels.

For example, three types of groupings are performed setting the value ofthe hyperparameter to “0.01”, “1”, and “100”.

In the embodiment, the model generation unit 23 generates the predictionmodel on a group by group basis for each of the several types ofgroupings by the grouping unit 27.

For example, the model generation unit 23 generates a prediction modelon a group by group basis for each of several types of groupings by thegrouping unit 27, and stores the generated prediction models in themodel storage unit 11.

Operation and Effect of the Second Embodiment

The grouping unit 27 groups users based on user information using aDirichlet process being a statistical technique. Accordingly,appropriate grouping can be performed automatically in accordance withthe user information. Consequently, the prediction accuracy of aprediction model can be improved without depending on the experience ofthe manager of the information processing apparatus 1.

Third Embodiment

Next, a third embodiment is described.

In the first embodiment, the model assignment unit 21 uses featuresvalue acquired from a user's click history to test a plurality ofprediction models, and assigns a prediction model suited to the user.However, in the third embodiment, the model assignment unit 21 assigns aprediction model in a method similar to the first embodiment for apredetermined period after the first time the model assignment processis executed, and assigns a prediction model based on related informationaccumulated for the predetermined period after a lapse of thepredetermined period. The other configurations are similar to those ofthe first embodiment.

In other words, in the embodiment, for a predetermined period (forexample, two weeks) after the first time the model assignment process isexecuted, the model assignment unit 21 uses the features value acquiredfrom the user's click history to test the plurality of prediction modelsstored in the model storage unit 11, and assigns a prediction modelsuited to the user in Step S14 as in the first embodiment.

Consequently, in Step S16, related information where the features valueacquired in Step S13 is associated with the prediction model assigned inStep S14 is accumulated in the related information storage unit 14 forthe predetermined period.

After the predetermined period has passed since the first time the modelassignment process was executed, in Step S14, the model assignment unit21 extracts a prediction model with high relativity with the acquiredfeatures and assigns the prediction model to the user based on aregularity of the relationship between the features and the predictionmodel in the predetermined period's related information accumulated inthe related information storage unit 14.

Operation and Effect of the Third Embodiment

The model assignment unit 21 assigns a prediction model suited to auser, based on related information after a lapse of a predeterminedperiod since the first time the model assignment process is executed.Accordingly, there is no need to test prediction models, and thecalculation time required to assign a prediction model can be reduced.

The embodiment is not limited to the above-mentioned embodiments, andalso includes modifications illustrated below within the scope that canachieve the object of the embodiment.

First Modification

In the embodiments, a prediction model is for calculating a predictionvalue related to a probability that a user clicks an advertisement.However, the embodiments are not limited to this. In other words, it issimply required to calculate a prediction value related to a probabilitythat a user performs an action on the Internet by operating the userterminal 2. The purchase of a product and the like can be exemplified assuch an action.

The information processing apparatus 1 may, for example, make variousrecommendations or suggestions to the user based on the calculatedprediction value.

Second Modification

In the embodiments, the model assignment process is performed for allusers whose click histories are accumulated in the action historystorage unit 12. However, the embodiments are not limited to this. Inother words, the model assignment process may be performed only for partof the users.

Third Modification

In the second embodiment, the grouping unit 27 performs grouping using aDirichlet process. However, the second embodiment is not limited tothis. For example, grouping may be performed using another statisticaltechnique.

Fourth Modification

In the third embodiment, the model assignment unit 21 uses featuresvalue acquired from a user's click history to test a plurality ofprediction models to assign a prediction model suited to the user for apredetermined period after the first time the model assignment processis executed. However, the third embodiment is not limited to this. Forexample, if related information where features are associated with aprediction model is created in advance from the experience or the likeof the manager of the information processing apparatus 1, a predictionmodel is extracted based on the related information to be assigned tothe user from the first time the model assignment process is executed.

In the present invention, a prediction model is generated for each groupcreated by grouping users based on user information. According to this,each prediction model calculates a prediction value for users having arelatively similar attribute. Accordingly, the number of pieces offeatures used to calculate a prediction value can be reduced as comparedto a case where a prediction value is calculated for all the users.

In the present invention, a prediction model selected from suchprediction models generated on a group by group basis is used tocalculate a prediction value. Accordingly, the time to calculate aprediction value can be reduced as compared to a case where a predictionmodel common to all the users is used to calculate a prediction value.

Although the invention has been described with respect to specificembodiments for a complete and clear disclosure, the appended claims arenot to be thus limited but are to be construed as embodying allmodifications and alternative constructions that may occur to oneskilled in the art that fairly fall within the basic teaching herein setforth.

What is claimed is:
 1. An information processing apparatus comprising: amodel generation unit configured to generate a prediction model forcalculating a prediction value related to a probability that a userperforms an action on the Internet by operating a user terminal, foreach group created by grouping users based on user information; a modelselection unit configured to select a prediction model suited to theuser from the prediction models; and a prediction value calculation unitconfigured to calculate the prediction value by using the predictionmodel selected by the model selection unit.
 2. The informationprocessing apparatus according to claim 1, wherein the model generationunit generates a common model common among the groups, as the predictionmodel.
 3. The information processing apparatus according to claim 1,comprising a model assignment unit configured to use a history ofactions that the user performed on the Internet by operating the userterminal to test the prediction models and assign a prediction modelsuited to the user, wherein the model selection unit selects theprediction model assigned to the user by the model assignment unit. 4.The information processing apparatus according to claim 3, wherein themodel assignment unit uses features value acquired from the history ofthe actions that the user performed on the Internet by operating theuser terminal to test the prediction models, assigns a prediction modelsuited to the user, generates related information where the featuresvalue is associated with the assigned prediction model, and accumulatesthe related information in a storage device, and after the relatedinformation is accumulated in the storage device for a predeterminedperiod, assigns a prediction model suited to the user based on theaccumulated related information.
 5. The information processing apparatusaccording to claim 1, comprising a model assignment unit configured toassign a prediction model suited to the user based on the relatedinformation indicating a relationship between the features value relatedto the actions that the user performed on the Internet by operating theuser terminal, and the prediction model, wherein the model selectionunit selects the prediction model assigned to the user by the modelassignment unit.
 6. The information processing apparatus according toclaim 1, wherein the model selection unit selects a prediction modelsuited to the user based on the related information indicating therelationship between the features value related to the actions that theuser performed on the Internet by operating the user terminal, and theprediction model.
 7. The information processing apparatus according toclaim 1, comprising an action history storage control unit configured toaccumulate, in a storage device, an action history being a history ofactions that the user performed on the Internet by operating the userterminal, wherein the action history storage control unit associates theaction history with the prediction model selected by the model selectionunit and accumulates the action history in the storage device, and themodel generation unit generates the prediction model based on acorresponding action history accumulated in the storage device.
 8. Theinformation processing apparatus according to claim 1, wherein the modelgeneration unit generates the prediction model for each group created bygrouping the users based on user attributes.
 9. The informationprocessing apparatus according to claim 1, comprising a grouping unitconfigured to group the users based on the user information, using aDirichlet process being a statistical technique, wherein the groupingunit sets a value of a parameter to determine the degree of gathering inthe Dirichlet process in several levels, and performs several types ofgroupings, and the model generation unit generates the prediction modelon a group by group basis for each of the several types of groupings bythe grouping unit.
 10. An information processing method to be executedby a computer, comprising: generating a prediction model for calculatinga prediction value related to a probability that a user performs anaction on the Internet by operating a user terminal, for each groupcreated by grouping users based on user information; selecting aprediction model suited to the user from the prediction models; andcalculating the prediction value by using the selected prediction model.11. A non-transitory computer-readable storage medium with an executableprogram stored thereon, wherein the program instructs a computer toperform: generating a prediction model for calculating a predictionvalue related to a probability that a user performs an action on theInternet by operating a user terminal, for each group created bygrouping users based on user information; selecting a prediction modelsuited to the user from the prediction models; and calculating theprediction value by using the selected prediction model.